whoamiAI: What 500 AI Sessions Taught Me About Myself
Published: March 15, 2025 (retrospective)
I’d spent 18 months feeding AI tools with my problems, code, and ideas. But what was the AI learning about me—and could I use that data to improve? Project whoamiAI exported and collated conversation data from Claude, Copilot, and Perplexity to surface personal insights on skills, training needs, and working patterns.
The Data Pipeline
Claude export → JSON normaliser
Copilot logs → JSON normaliser → Ollama analyser → insights.md
Perplexity → JSON normaliser
Public repo contains generic application code only. Personal data never leaves the local Proxmox VM—a design principle, not an afterthought.
Key Insights
- I over-engineer security — present in 78% of sessions. Feature, not bug.
- Delegation gaps — I defaulted to DIY when agent delegation was available. Fixed in Control Tower v2.
- Multi-agent thinking is now native — my problem decomposition style naturally maps to crew-based architectures.
Why This Matters
AI tools reflect your cognitive patterns back at you. Mining that data is a superpower for professional development—and a privacy minefield if done carelessly. Keeping it local via Ollama is non-negotiable.
Curious about your own AI patterns? whoamiAI is open source—star it on GitHub.
Next: The AI arms race accelerates—predictive cyber defence (Aug 2025).
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